Congfu Xu

Adaptive Bayesian personalized ranking for heterogeneous implicit feedbacks

Implicit feedbacks have recently received much attention in recommendation communities due to their close relationship with real industry problem settings. However, most works only exploit users’ homogeneous implicit feedbacks such as users’ transaction records from “bought” activities, and ignore the other type of implicit feedbacks like examination records from “browsed” activities. The latter are usually more abundant though they are associated with high uncertainty w.r.t. users’ true preferences. In this paper, we study a new recommendation problem called heterogeneous implicit feedbacks (HIF), where the fundamental challenge is the uncertainty of the examination records. As a response, we design a novel preference learning algorithm to learn a confidence for each uncertain examination record with the help of transaction records. Specifically, we generalize Bayesian personalized ranking (BPR), a seminal pairwise learning algorithm for homogeneous implicit feedbacks, and learn the confidence adaptively, which is thus called adaptive Bayesian personalized ranking (ABPR). ABPR has the merits of uncertainty reduction on examination records and accurate pairwise preference learning on implicit feedbacks. Experimental results on two public data sets show that ABPR is able to leverage uncertain examination records effectively, and can achieve better recommendation performance than the state-of-the-art algorithm on various ranking-oriented evaluation metrics.

The Smart Architect: Scalable Ontology-Based Modeling of Ancient Chinese Architectures

The smart architect is an innovative intelligent modeling system that can automatically generate architectures in the ancient Chinese architectural style using an ontology-based approach. Our approach aims to implement an architecture modeling system that can identify different elements and styles in a variety of buildings. It should also be able to generate numerous architectures of similar structures or styles based on the semantic knowledge extracted from existing buildings.

Semantic modeling for ancient architecture of digital heritage

Abstract In this paper, we propose a system for semantic modeling of southeast China vernacular urban. This system converts the basic modeling components of geometry units such as points, lines, and triangles, etc., into the semantic components such as streets, blocks, patches, and houses, etc. To implement the urban modeling system semantically, an XML based description and DTD [ISO/TC 211/WG 4/PT 19136 Geographic information—Geography Markup Language (GML) https://portal.opengeospatial.org/files/?artifact_id=7174 ] based verification technique is used to control the generation process of urban, and this DTD based verification technique can avoid the disadvantage of the grammar system and can ensure the coherent architecture styles of ancient vernacular houses. It has been applied to preserve the digital heritage of southeast China vernacular architecture.

Systems information in set pair analysis and its applications

The problem of uncertainty knowledge has been tackled for a long time by philosophers, logicians and mathematicians. Recently it becomes a crucial issue for computer scientists, particularly in the area of artificial intelligence (AI). The set pair analysis (SPA) theory, proposed by Keqin Zhao, is a novel uncertainty theory. The core of this theory is to consider certainties and uncertainties as a certain-uncertain system, and to depict uniformly all kinds of uncertainties such as random uncertainty, fuzzy uncertainty, indeterminate-known uncertainty, unknown and unexpected incident uncertainty, and uncertainty that results from imperfective information, using a connection degree formula that can fully embody its idea. SPA has been applied to many fields successfully such as industry, agriculture, forestry, education, physical education, military affairs, traffic, data fusion, decision-making, forecasting, comprehensive evaluation, and network planning, etc. The reason is that there exists abundant systems information such as system structure information, system theory information, etc, in SPA. In this paper, the systems information in SPA is discussed, and its applications are also given.

An Incremental Algorithm for Mining Privacy-Preserving Frequent Itemsets

Privacy preserving data mining is a novel research direction in data mining and statistical database, where data mining algorithms are analyzed for the side-effects they incur in data privacy. There have been many studies on efficient discovery of frequent itemsets in privacy preserving data mining. However, it is nontrivial to maintain such discovered frequent itemsets because a database may allow frequent itemsets updates and such frequent itemsets may be turned into infrequent itemsets. In this paper, an incremental updating algorithm IPPFIM is proposed for efficient maintenance of discovered frequent itemsets when new transaction data are added to a transaction database in privacy preserving. The algorithm makes use of previous mining results to cut down the cost of finding new frequent itemsets in an updated database, the performance evaluation shows the efficiency of this method

An adaptive fusion algorithm for spam detection

Spam detection has become a critical component in various online systems such as email services, advertising engines, social media sites, and so on. Here, the authors use email services as an example, and present an adaptive fusion algorithm for spam detection (AFSD), which is a general, content-based approach and can be applied to nonemail spam detection tasks with little additional effort. The proposed algorithm uses n-grams of nontokenized text strings to represent an email, introduces a link function to convert the prediction scores of online learners to become more comparable, trains the online learners in a mistake-driven manner via thick thresholding to obtain highly competitive online learners, and designs update rules to adaptively integrate the online learners to capture different aspects of spams. The prediction performance of AFSD is studied on five public competition datasets and on one industry dataset, with the algorithm achieving significantly better results than several state-of-the-art approaches, including the champion solutions of the corresponding competitions.

An Approach to Image Spam Filtering Based on Base64 Encoding and N-Gram Feature Extraction

As compared with text spam, the image spam is a variant which is invented to escape from traditional text-based spam classification and filtering. Various approaches to image spam filtering have been proposed with respective advantages and drawbacks in terms of time cost and efficiency. In this paper, we propose a new approach based on Base64 encoding of image files and

Understanding research field evolving and trend with dynamic Bayesian networks

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HGMF: Hierarchical Group Matrix Factorization for Collaborative Recommendation

-gram technique for feature extraction. By transforming normal images into Base64 presentation, we try to extract features of an image with

Combining KNN algorithm and other classifiers

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